Tidy labels in census tables

Some table labels are quite verbose, and users will often want to shorten them. These functions make tidying common types of labels easy. Most produce straightforward output, but there are several more generic tidiers:

• tidy_simplify() attempts to simplify labels by removing words common to all labels.

• tidy_parens() attempts to simplify labels by removing all terms in parentheses.

• tidy_race_detailed() creates logical columns for each of the six racial categories.

Usage

tidy_race(x)

tidy_race_detailed(x, x2, x3)

tidy_ethnicity(x)

tidy_age(x)

tidy_age_bins(x, as_factor = FALSE)

tidy_income_bins(x, as_factor = FALSE)

tidy_simplify(x)

tidy_parens(x)

Arguments

x

A factor, which will be re-leveled. Character vectors will be converted to factors.

x2, x3

Additional character columns containing detailed information for certain variables (e.g. detailed race)

as_factor

if TRUE, return a factor with levels of the form [35,40].

Value

A re-leveled factor, except for tidy_age_bins(), which by default returns a data frame with columns age_from and age_to (inclusive).

Examples

ex_race_long = c("american indian and alaska native alone", "asian alone",
    "black or african american alone", "hispanic or latino",
    "native hawaiian and other pacific islander alone",
    "some other race alone", "total", "two or more races",
    "white alone", "white alone, not hispanic or latino")
tidy_race(ex_race_long)
#>  [1] aian     asian    black    hisp     nhpi     other    total    two     
#>  [9] white    white_nh
#> Levels: aian asian black hisp nhpi other total two white white_nh

tidy_age_bins(c("10 to 14 years", "21 years", "85 years and over"))
#> # A tibble: 3 × 2
#>   age_from age_to
#>      <dbl>  <dbl>
#> 1       10     14
#> 2       21     21
#> 3       85    Inf

tidy_parens(c("label one (fake)", "label two (fake)"))
#> [1] label one label two
#> Levels: label one label two
tidy_simplify(c("label one (fake)", "label two (fake)"))
#> [1] one two
#> Levels: one two

if (FALSE)  # requires API key
d = cens_get_acs("B02003", "us", year=2019, survey="acs1")
dplyr::mutate(d, tidy_race_detailed(dtldr_1, dtldr_2, dtldr_3))
#> Error: object 'd' not found
 # \dontrun{}